Biomedical engineering advances最新文献

Audio-based deep learning classification of laryngeal pathologies with detection of precancerous and cancerous lesions using Gammatone Cepstral coefficients 基于音频的深度学习喉部病变分类，使用伽玛酮倒谱系数检测癌前病变和癌性病变

Biomedical engineering advances

Pub Date : 2026-01-19 DOI: 10.1016/j.bea.2026.100211

Julia Zofia Tomaszewska , Wojciech Kukwa , Apostolos Georgakis

Introduction

Despite extensive research on audio-based voice pathology detection, current literature lacks clear and consistent evidence identifying acoustic features capable of reliably discriminating precancerous and cancerous laryngeal lesions, particularly when analysed using continuous speech signals.

Problem statement

The performance of audio-based laryngeal pathology classification systems on continuous speech remains significantly underreported, and commonly used Mel-Frequency Cepstral Coefficients (MFCCs) may be suboptimal for capturing pathology-related acoustic characteristics.

Objectives

This study investigates the hypothesis that continuous speech audio signals analysed with Gammatone Cepstral Coefficients (GTCCs) enable the accurate and precise detection of laryngeal pathologies, with the specific focus on precancerous and cancerous lesions.

Methods

An audio-based classification system employing GTCCs for feature extraction and a one-dimensional Convolutional Neural Network (CNN) for classification is proposed. The system considers three classes: precancerous and cancerous lesions, neuromuscular disorders, and healthy cases. Performance was evaluated using two datasets: a custom speech dataset collected for this research and the Saarbruecken Voice Database (SVD).

Results

GTCCs derived from speech signals delivered superior classification accuracy compared to the widely used Mel-Frequency Cepstral Coefficients (MFCCs). On the custom dataset, the proposed method achieved an average classification accuracy of 85.04% ±1.23 compared to 63.22% ± 1.62 using MFCCs. On SVD, GTCCs achieved 73.93% ±1.42, compared to 60.36% ±2.44 for MFCCs. The statistical significance of the obtained results was evidenced using t-test with the significance level set at 1%.

Conclusions

The results demonstrate that GTCCs extracted from continuous speech signals provide a robust and effective representation for audio-based laryngeal pathology classification, highlighting their potential for use in automated pre-screening systems targeting precancerous and cancerous voice disorders.

尽管对基于音频的语音病理检测进行了广泛的研究，但目前的文献缺乏明确和一致的证据来识别能够可靠地区分癌前病变和癌性喉部病变的声学特征，特别是在使用连续语音信号进行分析时。基于音频的喉病理分类系统在连续语音上的表现仍然被严重低估，通常使用的Mel-Frequency倒谱系数（MFCCs）可能不是捕获病理相关声学特征的最佳选择。目的：本研究探讨了用伽玛酮倒谱系数（gtcc）分析连续语音音频信号能够准确和精确地检测喉部病变，特别是癌前病变和癌性病变的假设。方法提出了一种基于音频的分类系统，采用gtcc进行特征提取，一维卷积神经网络（CNN）进行分类。该系统考虑了三类：癌前病变和癌性病变、神经肌肉疾病和健康病例。使用两个数据集评估性能：为本研究收集的自定义语音数据集和Saarbruecken语音数据库（SVD）。结果基于语音信号的gtcc比常用的Mel-Frequency倒谱系数（mfcc）具有更好的分类精度。在自定义数据集上，该方法的平均分类准确率为85.04%±1.23，而使用mfc的平均分类准确率为63.22%±1.62。在SVD上，gtcc为73.93%±1.42，而mfcc为60.36%±2.44。所得结果的统计学显著性采用t检验，显著性水平设为1%。结果表明，从连续语音信号中提取的gtcc为基于音频的喉部病理分类提供了稳健有效的表征，突出了其在针对癌前和癌性语音疾病的自动预筛查系统中的应用潜力。

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引用次数: 0

Influence of stance width and foot rotation on muscle activity and ground reaction forces during squats in female lacrosse players 站立宽度和足部旋转对女子长曲棍球运动员深蹲时肌肉活动和地面反作用力的影响

Biomedical engineering advances

Pub Date : 2026-01-19 DOI: 10.1016/j.bea.2026.100210

Anuradhi Bandara, Shinichi Kawamoto, Mona Makita, Momoko Nagai-Tanima, Tomoki Aoyama

Female lacrosse players experience a high burden of non-contact lower-extremity injuries, and squat-based neuromuscular training is commonly used to develop lower-limb strength, coordination, and load tolerance. How common technique modifications such as stance width and foot rotation affect joint excursion, muscle activation, ground reaction forces (GRFs), and inter-limb loading symmetry during squats in this population remains unclear. This study aimed to examine the effects of stance width and foot rotation on lower-limb joint excursion, surface electromyography (sEMG), GRFs, and inter-limb GRF asymmetry during bodyweight squats in female lacrosse players. Ten Japanese university-level female lacrosse players performed squats under six stance conditions (narrow, shoulder-width, wide × parallel or external rotation). sEMG was recorded from ten lower-limb muscles, synchronized with 3D kinematics and bilateral force plates. Friedman tests with false discovery rate (FDR) correction evaluated stance-related differences, with effect sizes estimated using Kendall’s W. Stance significantly influenced hip (χ² = 33.31, p < 0.001, W = 0.67), knee (χ² = 19.94, p = 0.001, W = 0.40), and ankle (χ² = 24.23, p < 0.001, W = 0.49) joint excursion. Wide external rotation (WidER) yielded the greatest hip joint flexion–extension excursion (116.9° ± 7.9°), whereas narrow parallel stance (NarPar) produced the smallest (98.2° ± 4.8°). During the descending phase, gluteus maximus activation was significantly higher in wide stances compared with narrow and shoulder-width conditions (q < 0.013). GRFs showed consistent vertical peaks across stances (∼56–62 % body weight), but mediolateral peaks were substantially higher in WidER (∼17 % body weight) than in narrow stances (∼5–7 % body weight). In female lacrosse players, squat stance meaningfully modulates mechanics even under bodyweight loading. WidER squats preferentially increase hip excursion, gluteus maximus activation, and global mediolateral GRFs, whereas narrow parallel squats increase ankle dorsiflexion demands and are associated with greater vertical loading asymmetry. These findings support tailoring stance width and foot rotation to target hip-dominant strength and frontal-plane control versus ankle mobility demands within lacrosse-oriented neuromuscular training.

女性长曲棍球运动员承受着非接触性下肢损伤的沉重负担，而蹲式神经肌肉训练通常用于发展下肢力量、协调性和负荷耐受性。在这一人群中，蹲姿宽度和足部旋转等常见的技术修改如何影响关节偏移、肌肉激活、地面反作用力（GRFs）和下肢间负荷对称仍不清楚。本研究旨在探讨站立宽度和足部旋转对女性长冰球运动员体重深蹲时下肢关节偏移、表面肌电图（sEMG）、GRF和肢体间GRF不对称的影响。10名日本大学水平的女子长曲棍球运动员在六种姿势条件下（窄、肩宽、宽×平行或外旋）进行深蹲。记录10块下肢肌肉的肌电图，与3D运动学和双侧力板同步。错误发现率（FDR）校正的Friedman检验评估了与姿态相关的差异，使用Kendall的W来估计效应量。姿态显著影响髋关节（χ²= 33.31,p < 0.001, W = 0.67）、膝关节（χ²= 19.94,p = 0.001, W = 0.40）和踝关节（χ²= 24.23,p < 0.001, W = 0.49）关节偏移。宽外旋（WidER）产生最大的髋关节屈伸偏移（116.9°±7.9°），而窄平行站立（NarPar）产生最小的髋关节屈伸偏移（98.2°±4.8°）。在下降阶段，宽姿势的臀大肌激活明显高于窄姿势和肩宽姿势（q < 0.013）。GRFs在不同体位表现出一致的垂直峰（~ 56 - 62%体重），但较宽体位（~ 17%体重）的中外侧峰明显高于较窄体位（~ 5 - 7%体重）。在女子曲棍球运动员中，即使在体重负荷下，深蹲姿势也有意义地调节力学。宽深蹲优先增加髋偏移、臀大肌激活和全局中外侧GRFs，而窄平行深蹲增加踝关节背屈需求，并与更大的垂直负荷不对称相关。这些发现支持在以长曲棍球为导向的神经肌肉训练中，调整立场宽度和足部旋转来针对髋关节主导力量和额平面控制，而不是踝关节活动需求。

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引用次数: 0

Multimodal EMG–IMU sensor fusion with dual-output LSTM for fatigue estimation during neonatal chest compressions 多模态肌电- imu传感器融合双输出LSTM用于新生儿胸外按压疲劳评估

Biomedical engineering advances

Pub Date : 2026-01-16 DOI: 10.1016/j.bea.2026.100209

Prashant Purohit , John R. LaCourse

Background

During neonatal cardiopulmonary resuscitation (NCPR), rescuer fatigue develops rapidly and compromises compression quality. Conventional feedback systems infer fatigue indirectly from mechanics (depth/rate) and may miss early neuromuscular changes.

Objective

To develop and evaluate a multimodal framework that fuses surface EMG (physiology) and IMU (biomechanics) to improve the accuracy of (i) fatigue level classification and (ii) prediction of fatigue-onset time during neonatal chest compressions (NCPR).

Methods

Twenty trained providers performed simulated neonatal compressions on a manikin while synchronized EMG (deltoid, triceps, upper trapezius) and 3-axis IMU signals were recorded and windowed (2 s, 50% overlap). Features included EMG RMS, MAV, median frequency (MF), and IMU depth dynamics. A dual-output Long Short-Term Memory (LSTM) jointly produced 3-class fatigue labels and onset-time regression.

Results

Fusion outperformed unimodal models: 98.3% accuracy, macro-F1 0.982, AUC 0.99; onset prediction RMSE 38.3 s, R² 0.68. EMG-only: 69.4% accuracy; IMU-only: 96.7%. EMG provided early physiological fatigue signatures, complementing IMU mechanical degradation.

Conclusion

EMG–IMU fusion with temporal deep learning improves fatigue estimation during NCPR and is suitable for real-time feedback to support optimal rescuer rotation. Earlier, physiology-aware fatigue detection enables proactive team management before compression quality declines. Lightweight LSTM fusion runs in real time and generalizes across rescuers.

背景：在新生儿心肺复苏（NCPR）过程中，急救人员疲劳迅速发展并影响按压质量。传统的反馈系统间接地从力学（深度/速率）推断疲劳，可能会错过早期的神经肌肉变化。目的开发和评估融合体表肌电图（生理学）和IMU（生物力学）的多模态框架，以提高新生儿胸外按压（NCPR）过程中疲劳水平分类和疲劳发作时间预测的准确性。方法20名训练有素的医护人员在人体模型上进行模拟新生儿按压，同时记录同步肌电信号（三角肌、三头肌、上斜方肌）和3轴IMU信号并加窗（2秒，50%重叠）。特征包括EMG RMS， MAV，中位数频率（MF）和IMU深度动态。双输出长短期记忆（LSTM）联合生成3类疲劳标签和发病时间回归。结果融合优于单峰模型：准确率为98.3%，宏观f1为0.982，AUC为0.99；发病预测RMSE为38.3 s, R²0.68。仅肌电图：准确率69.4%；IMU-only: 96.7%。肌电图提供了早期生理疲劳特征，补充了IMU的机械退化。结论emg - imu与时间深度学习的融合改善了NCPR过程中的疲劳估计，适合于实时反馈，以支持最优的救援人员轮换。早些时候，生理疲劳检测可以在压缩质量下降之前进行前瞻性的团队管理。轻量级LSTM融合实时运行，并在救援人员之间进行推广。

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引用次数: 0

Lubrication to reduce tissue shear loading - effects on comfort perception and tissue damage when wearing a facemask 润滑以减少组织剪切负荷-对舒适度的影响和戴口罩时的组织损伤

Biomedical engineering advances

Pub Date : 2026-01-15 DOI: 10.1016/j.bea.2026.100208

Urvi Sonawane , Jack Hayes , Mary J. Morrell , Marc A. Masen

Patients with respiratory disease often require breathing support delivered via a nasal or facemask. The use of such a mask causes continued loading of the skin which can result in discomfort or skin injury, which may lead to low adherence to treatment. It is hypothesised that lubricating the mask-skin interface will reduce shear stress, thus reducing the load on the skin. The aim of this study was to determine whether the application of a novel lubricant to the mask improved discomfort and/or skin injury measured using three outcomes: subjective comfort, erythema, and the presence of epidermal interleukins.

Ten healthy participants were randomised to wear a lubricated or unlubricated facemask for one hour. Each participant switched over after a one-hour washout period. Subjective comfort was measured using visual analogue scales. Erythema was quantified from facial photographs and interleukins were obtained using tape stripping and ELISA assay analysis. Results show that the subjective comfort significantly improved after one hour with the application of a novel lubricant, compared to no lubrication (p=0.015), however erythema and interleukins were not significantly different. As comfort perception may affect adherence, this novel lubricant may be beneficial in the clinical care of those needing to wear facemasks.

呼吸系统疾病患者通常需要通过鼻罩或面罩给予呼吸支持。使用这种口罩会导致皮肤持续负荷，从而导致不适或皮肤损伤，这可能导致治疗依从性低。假设润滑面膜-皮肤界面将减少剪应力，从而减少皮肤上的负荷。本研究的目的是确定一种新型润滑剂在口罩上的应用是否改善了不适和/或皮肤损伤，使用三个结果来测量：主观舒适度、红斑和表皮白细胞介素的存在。10名健康的参与者被随机分配戴润滑或不润滑的口罩一小时。每个参与者在一个小时的洗脱期后都转换了。主观舒适度采用视觉模拟量表进行测量。用面部照片定量红斑，用胶带剥离法和ELISA法测定白细胞介素。结果表明，与未润滑相比，使用新型润滑液1 h后患者的主观舒适度显著提高（p=0.015），但红斑和白细胞介素无显著差异。由于舒适度可能会影响依从性，这种新型润滑剂可能对那些需要戴口罩的临床护理有益。

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引用次数: 0

Suture-less vascular anastomosis – State of the art, challenges and perspectives: A review 无缝线血管吻合技术的现状、挑战与展望

Biomedical engineering advances

Pub Date : 2026-01-13 DOI: 10.1016/j.bea.2026.100207

John Ser Pheng Loh , Yijia Yuan , Kuan-Che Feng , Robert Heymann , Justin Kok Soon Tan , Lars Rasmusson , Hwa Liang Leo , Reinhilde Jacobs

Vascular microanastomosis is a cornerstone of reconstructive, transplantation, and cardiothoracic surgery, enabling the reconnection of tiny blood vessels to restore circulation to transplanted tissues or donor organs. Traditionally, this delicate process relies on hand suturing under microscopic magnification, a demanding and time-consuming technique that requires extensive training and precision. Despite decades of refinement, conventional suturing remains limited by human factors. Technical errors can lead to thrombosis, tissue necrosis, hematoma, or flap loss, with serious implications for patient outcomes and healthcare costs. In response, suture-less anastomosis methods have emerged as promising alternatives. These devices use mechanical couplers, rings, clips, magnets, or bioengineered scaffolds to connect vessel ends rapidly and reproducibly, aiming to reduce operative time, minimize ischemia, and improve procedural consistency across surgical teams. Recent innovations have introduced biodegradable and intraluminal designs that reduce foreign-body reactions, lower palpability, and accommodate vessel growth, offering distinct advantages in paediatric and long-term reconstructive settings.

Despite these advances, the widespread clinical adoption of suture-less technologies remains constrained by unresolved challenges. Key considerations include ensuring mechanical stability under physiological pulsation, optimizing biocompatibility to prevent thrombosis at the junction, and adapting device geometries to the diversity of vessel sizes and wall structures encountered in clinical practice. Continued translational research is needed to refine materials, simplify deployment mechanisms, and integrate these systems seamlessly into microsurgical workflows. This review synthesizes current developments in suture-less vascular anastomosis, critically evaluating their benefits and limitations across experimental and clinical studies. It also identifies future research priorities at the intersection of materials science, additive manufacturing, and surgical engineering. As these disciplines converge, next-generation suture-less devices hold the potential to redefine vascular repair by making micro-anastomosis faster, safer, and more accessible, thus transforming reconstructive and transplant surgery for patients who depend on these life-saving procedures.

血管微吻合是重建、移植和心胸外科手术的基石，它使微小血管重新连接，以恢复移植组织或供体器官的循环。传统上，这种精细的过程依赖于显微镜放大下的手工缝合，这是一种要求高且耗时的技术，需要大量的训练和精度。尽管经过了几十年的改进，传统缝合术仍然受到人为因素的限制。技术错误可能导致血栓形成、组织坏死、血肿或皮瓣丢失，严重影响患者预后和医疗保健费用。因此，无缝线吻合术成为一种很有前途的选择。这些设备使用机械耦合器、环、夹子、磁铁或生物工程支架快速、可重复地连接血管末端，旨在减少手术时间，最大限度地减少缺血，并提高手术团队的程序一致性。最近的创新引入了可生物降解和腔内设计，减少了异物反应，降低了触感，并适应了血管生长，在儿科和长期重建环境中具有明显的优势。尽管取得了这些进展，无缝线技术的广泛临床应用仍然受到尚未解决的挑战的制约。关键考虑因素包括确保生理脉动下的机械稳定性，优化生物相容性以防止连接处血栓形成，以及适应临床实践中遇到的血管大小和壁结构的多样性。需要继续进行转化研究，以改进材料，简化部署机制，并将这些系统无缝集成到显微外科工作流程中。本文综述了无缝线血管吻合术的最新进展，在实验和临床研究中对其优点和局限性进行了批判性评价。它还确定了材料科学、增材制造和外科工程交叉领域未来的研究重点。随着这些学科的融合，下一代无缝线装置有可能通过使微吻合更快、更安全、更容易获得来重新定义血管修复，从而改变依赖这些挽救生命的手术的患者的重建和移植手术。

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引用次数: 0

Classification of PSQA outcomes in prostate VMAT treatments: a comparative study of machine learning models 前列腺VMAT治疗中PSQA结果的分类：机器学习模型的比较研究

Biomedical engineering advances

Pub Date : 2026-01-09 DOI: 10.1016/j.bea.2026.100206

Francis C. Djoumessi Zamo , Alexandre Ngwa Ebongue , Daniel Bongue , Maurice Moyo Ndontchueng , Christopher F. Njeh

Objective

The present study aimed to construct and compare machine learning (ML) models for classifying patient-specific quality assurance (PSQA) outcomes in prostate volumetric modulated arc therapy (VMAT) treatment.

Methods

A total of 1247 prostate VMAT plans were retrospectively analyzed and several metrics and anatomical information extracted from the RT-plans files and contours were used as predictive variables. The following machine learning (ML) models: Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Gradient Boosting (GB), AdaBoost (AB), k-Nearest Neighbors (KNN), Naïve Bayes (NB) and Neural Network (NN) were developed to classify the PSQA outcomes, and their performances compared with different metrics.

Results

The results demonstrated that Random Forest and Gradient Boosting achieved the highest classification accuracy with area under the curve (AUC) values of 0.95 and 0.96, respectively and Accuracy of 0.91 for both classifiers. These models effectively balanced precision and accuracy while minimizing false negative and false positives rates which is critical for identifying potentially unsafe treatment plans.

Conclusion

ML-based PSQA classification (Random Forest and Gradient Boosting) demonstrates strong potential for optimizing quality assurance in prostate VMAT treatments. By integrating predictive analytics into clinical workflows, radiation oncology departments can improve efficiency, reduce resource demands, and enhance patient safety, paving the way for more adaptive and automated QA protocols.

目的建立前列腺体积调节弧线治疗（VMAT）患者特异性质量保证（PSQA）结果分类的机器学习（ML）模型并进行比较。方法回顾性分析1247例前列腺VMAT图像，并以图像文件和等高线提取的指标和解剖信息作为预测变量。开发了以下机器学习（ML）模型：逻辑回归（LR）、决策树（DT）、随机森林（RF）、梯度增强（GB）、AdaBoost （AB）、k-近邻（KNN）、Naïve贝叶斯（NB）和神经网络（NN）对PSQA结果进行分类，并将其性能与不同指标进行比较。结果随机森林和梯度增强的分类准确率最高，曲线下面积（AUC）分别为0.95和0.96，准确率为0.91。这些模型有效地平衡了精度和准确性，同时最大限度地减少假阴性和假阳性率，这对于识别潜在的不安全治疗计划至关重要。结论基于ml的PSQA分类（随机森林和梯度增强）在前列腺VMAT治疗的质量保证优化方面具有很大的潜力。通过将预测分析集成到临床工作流程中，放射肿瘤科可以提高效率，减少资源需求，增强患者安全性，为更具适应性和自动化的QA协议铺平道路。

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引用次数: 0

Use of the Geometric Pattern Transformation to detect diabetes mellitus type 2 from blood glucose signals 利用几何模式变换从血糖信号中检测2型糖尿病

Biomedical engineering advances

Pub Date : 2025-12-25 DOI: 10.1016/j.bea.2025.100205

Marcos Maillot , Ariel Amadio , Cristian Bonini , Leandro Robles Dávila , Walter Legnani , Dino Otero

Purpose

Type 2 Diabetes Mellitus (DMT2) is a disease with a high incidence worldwide, and various estimates project an increase in the near future. This research introduces a new methodology based on blood glucose concentration recordings obtained from Continuous Glucose Monitors (CGM) for the detection of the disease. The main research question is whether it is possible to enhance the detection ability of DMT2 by applying Geometric Pattern Transformation (GPT) to the glucose records, compared to basic statistical tools.

Methods

The standard deviation of the glucose signal from continuous glucose monitoring (CGM) was evaluated as a parameter to distinguish between individuals with and without diabetes. The GPT technique was then applied to the glucose data to assess its effectiveness in enhancing disease detection compared to traditional statistical methods.

Results

The findings indicate that traditional statistical tools are insufficient to achieve the same performance as the proposed method for detecting DMT2. Applying GPT significantly improved detection accuracy, showing a clear advantage in differentiating between subjects with and without DMT2. Moreover, glucose monitoring over a period of at least 3 days proved to be as effective as longer periods for detection purposes.

Conclusions

The proposed methodology, using basic mathematical operations on glucose data, effectively distinguishes individuals with DMT2 from those without. The parameters used to assess detection quality demonstrate a marked improvement due to GPT. Additionally, a 3-day monitoring period is sufficient for reliable detection, potentially streamlining the diagnostic process.

2型糖尿病（DMT2）是一种世界范围内高发的疾病，各种估计在不久的将来会增加。本研究介绍了一种基于连续血糖监测仪（CGM）获得的血糖浓度记录的新方法，用于检测该疾病。主要的研究问题是，与基本的统计工具相比，是否有可能通过对葡萄糖记录应用几何模式变换（GPT）来增强DMT2的检测能力。方法以连续血糖监测（CGM）血糖信号的标准差作为区分糖尿病患者和非糖尿病患者的指标。然后将GPT技术应用于葡萄糖数据，以评估其与传统统计方法相比在增强疾病检测方面的有效性。结果传统的统计工具不足以达到与所提方法相同的检测DMT2的性能。应用GPT可显著提高检测精度，在区分DMT2患者和非DMT2患者方面具有明显优势。此外，至少3天的血糖监测被证明与更长时间的监测一样有效。结论该方法利用葡萄糖数据的基本数学运算，可有效区分DMT2患者和非DMT2患者。用于评估检测质量的参数表明，由于GPT的显著改善。此外，3天的监测周期足以进行可靠的检测，从而可能简化诊断过程。

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引用次数: 0

A novel pipeline for converting surface electromyography signals into muscle activations 一种将表面肌电信号转化为肌肉激活的新型管道

Biomedical engineering advances

Pub Date : 2025-12-11 DOI: 10.1016/j.bea.2025.100204

P. Tsakonas , N.D. Evans , J. Hardwicke , M.J. Chappell

This study introduces a novel pipeline for converting surface electromyography (sEMG) signals into muscle activations using the Hilbert-Huang Transform. Traditional approaches in this context often apply low-pass filters that suppress high-frequency components, potentially discarding physiologically relevant signal information. In contrast, the proposed method leverages Empirical Mode Decomposition and Hilbert spectral analysis to preserve the nonstationary and multi-frequency nature of sEMG data. Activation outputs are then mapped through physiologically inspired dynamics, yielding time-resolved muscle activations. Comparative analyses were conducted across three muscles (EDC, FDS, FDP) using data from 10 subjects each performing 5 cylindrical grasps. Intra-subject comparisons using Wilcoxon signed-rank tests revealed statistically significant improvements (p < 0.001) in nearly all trials. Linear mixed-effects analysis of log-transformed activations showed that the new pipeline yields significantly higher muscle activations within each muscle: EDC GMR = 1.31 (95% CI: 1.255–1.359), FDS GMR = 1.35 (95% CI: 1.296–1.396), and FDP GMR = 1.29 (95% CI: 1.248–1.342), all p < 0.001. These results suggest that the choice of sEMG processing pipeline can meaningfully alter activation estimates and potentially influence musculoskeletal model estimation. The method presented provides a robust and physiologically consistent alternative for applications in biomechanics, prosthetic control, and neuromuscular modelling.

本研究介绍了一种利用Hilbert-Huang变换将表面肌电图（sEMG）信号转换为肌肉激活的新管道。在这种情况下，传统的方法通常使用低通滤波器来抑制高频成分，可能会丢弃生理相关的信号信息。相比之下，该方法利用经验模态分解和希尔伯特谱分析来保持表面肌电信号数据的非平稳和多频特性。激活输出然后通过生理激发动力学映射，产生时间分辨的肌肉激活。对比分析了三个肌肉（EDC， FDS， FDP），使用来自10名受试者的数据，每个受试者进行5个圆柱形抓握。使用Wilcoxon符号秩检验的受试者内比较显示，几乎所有试验均有统计学显著改善（p < 0.001）。对数转换激活的线性混合效应分析显示，新管道在每个肌肉中产生显著更高的肌肉激活：EDC GMR = 1.31 (95% CI: 1.255-1.359), FDS GMR = 1.35 (95% CI: 1.296-1.396), FDP GMR = 1.29 (95% CI: 1.247 - 1.342)，均p <； 0.001。这些结果表明，表面肌电信号处理管道的选择可以有意地改变激活估计，并可能影响肌肉骨骼模型的估计。所提出的方法为生物力学、假肢控制和神经肌肉建模的应用提供了一种健壮的、生理上一致的替代方法。

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引用次数: 0

OMG-RL: Offline Model-based Guided Reward Learning for heparin treatment OMG-RL：基于离线模型的肝素治疗引导奖励学习

Biomedical engineering advances

Pub Date : 2025-11-01 DOI: 10.1016/j.bea.2025.100198

Yooseok Lim , Sujee Lee

Accurate medication dosing plays a critical role in the therapeutic process. Recent studies have investigated the application of Reinforcement Learning (RL) to optimize administration strategies. In RL, the definition of a reward function is indispensable; however, reliance on a limited set of explicitly defined rewards fails to capture the heterogeneity of clinical characteristics across patients. Moreover, the wide range of medications used in practice makes it impractical to design a dedicated reward function for each drug. To address this challenge, we propose learning a reward network that reflects clinicians’ therapeutic intentions, moving beyond predefined reward functions. In this study, we introduce Offline Model-based Guided Reward Learning (OMG-RL), an offline Inverse Reinforcement Learning (IRL) method. By incorporating a dynamic model into sample-based Maximum Entropy IRL, the method learns a parametrized reward network from offline data, enhancing the agent’s policy. We validate OMG-RL on the clinically significant heparin dosing task. Our results demonstrate that the agent is positively reinforced not only in terms of the recovered reward but also in activated partial thromboplastin time (aPTT), a key laboratory test for monitoring heparin effects. These findings suggest that the OMG-RL effectively captures clinicians’ therapeutic intentions. More broadly, our approach provides a general framework for RL-based medication dosing.

准确给药在治疗过程中起着至关重要的作用。最近的研究探讨了强化学习（RL）在优化管理策略中的应用。在强化学习中，奖励函数的定义是必不可少的；然而，依赖于一组有限的明确定义的奖励未能捕捉到患者临床特征的异质性。此外，实践中使用的药物范围广泛，因此为每种药物设计专门的奖励功能是不切实际的。为了应对这一挑战，我们建议学习一个反映临床医生治疗意图的奖励网络，超越预定义的奖励功能。在本研究中，我们引入了离线基于模型的引导奖励学习（OMG-RL），这是一种离线逆强化学习（IRL）方法。该方法通过将动态模型融入到基于样本的最大熵IRL中，从离线数据中学习参数化奖励网络，增强智能体的策略。我们在具有临床意义的肝素给药任务上验证了OMG-RL。我们的研究结果表明，药物不仅在恢复奖励方面，而且在活化的部分凝血活素时间（aPTT）方面也有积极的增强作用，aPTT是监测肝素作用的关键实验室测试。这些发现表明，OMG-RL有效地捕捉了临床医生的治疗意图。更广泛地说，我们的方法为基于rl的药物剂量提供了一个总体框架。

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引用次数: 0

Machine learning based radiomics analysis of SPECT images in predicting neuroendocrine tumors 基于机器学习的SPECT图像放射组学分析预测神经内分泌肿瘤

Biomedical engineering advances

Pub Date : 2025-11-01 DOI: 10.1016/j.bea.2025.100197

Mehdi Ghazizadeh , Mahasti Amoui , Mohammad Reza Deevband , Kamran Aryana , Farivash Karamian , Abolhasan Divband , Zahra Mahboubi-Fooladi , Alireza Montazerabadi , Esmail Jafari , Alireza Zirak , Meysam Tavakoli

Purpose

Over the past few decades, the prevalence of neuroendocrine tumors (NETs) has significantly increased. Current pathological assessment methods often rely on invasive procedures. This study aims to develop machine learning (ML)-based radiomics models using SPECT images to predict grade 1 and 2 NETs and assess the Ki-67 index, particularly in cases with grade discrepancy.

Materials and Methods

We developed a retrospective research involving 144 patients with pathologically confirmed NETs collected from five nuclear medicine centers. The dataset included patients with available Ki-67 index values and those exhibiting discrepancies between their Ki-67 index and tumor grade (i.e., low Ki-67 index and intermediate grade). Radiomic features were extracted from SPECT images, and feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. Radiomic signature models were then constructed using a decision tree classifier.

Results

The prediction of tumor grade in absence of Ki-67 index resulted in area under the curve (AUC) of 0.83, in the presence of Ki-67 index with discrepancy an AUC of 0.86, and in presence of Ki-67 index without discrepancy an AUC of 0.99.

Conclusion

The proposed radiomics model based on SPECT images, and machine learning can effectively predict and assess classification of NET grade. Although Ki-67 has been highly recommended as a strong predictive feature in previous studies, our findings suggest that its predictive value diminishes in cases with grade discrepancies, such as low Ki-67 index paired with intermediate grade.

目的在过去的几十年里，神经内分泌肿瘤（NETs）的患病率显著增加。目前的病理评估方法往往依赖于侵入性手术。本研究旨在开发基于机器学习（ML）的放射组学模型，使用SPECT图像预测1级和2级NETs，并评估Ki-67指数，特别是在分级差异的情况下。材料与方法我们开展了一项回顾性研究，纳入了来自5个核医学中心的144例病理证实的NETs患者。该数据集包括具有可用Ki-67指数值的患者以及Ki-67指数与肿瘤分级（即低Ki-67指数和中等分级）之间存在差异的患者。从SPECT图像中提取放射学特征，并使用最小绝对收缩和选择算子（LASSO）方法进行特征选择。然后使用决策树分类器构建放射性特征模型。结果无Ki-67指数时预测肿瘤分级的曲线下面积（AUC）为0.83，有Ki-67指数时预测的AUC为0.86，无Ki-67指数时预测的AUC为0.99。结论基于SPECT图像和机器学习的放射组学模型可以有效预测和评估NET分级。虽然Ki-67在之前的研究中被强烈推荐为一种强有力的预测特征，但我们的研究结果表明，在等级差异的情况下，如低Ki-67指数与中等等级配对，其预测价值会降低。

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引用次数: 0